Improved Retinex and Multi-Image Fusion Algorithm for Low Illumination Image Enhancemen
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摘要: 为了解决低照度图像在图像增强过程中图像质量不佳、对比度不高等问题，本文提出改进Retinex与多图像融合算法用于低照度图像增强。首先将待处理图像转换到HSV色彩空间，并设定阈值对其V通道分量进行亮度调节，然后转换到RGB色彩空间，将其拷贝3份，对第一份进行直方图均衡化，中值滤波处理；对第2份进行自动亮度调节，双边滤波处理；对第3份进行改进的Retinex算法处理，采用高斯滤波、双边滤波作为其环绕函数，估计图像照明分量，最后输出反射图。将处理后的3份图像转到HSV色彩空间，对其V分量进行多图像融合，H、S分量沿用第2份图像分量值，最后将融合后的图像由HSV转为RGB色彩空间，输出处理后的图像。实验结果表明，本文提出的算法在增强低照度图像的同时，还可抑制图像噪声，同时具有良好的保边性，且细节明显。Abstract: To solve the problems of poor image quality and low contrast in low-illumination image enhancement, this study proposes an improved Retinex and multi-image fusion algorithm for low -illumination image enhancement. First, the image to be processed is converted to the HSV color space, and the brightness of the V-channel component is adjusted by setting a threshold. Then, it is converted to the RGB color space, and three copies are made. Histogram equalization and median filtering are performed for the first part; the second part is processed by automatic brightness adjustment and bilateral filtering; the third part is processed by an improved Retinex algorithm, which uses Gaussian filtering and bilateral filtering as its surround function to estimate the illumination component of the image, and outputs the reflection image. The three processed images are transferred to the HSV color space, and the V component is fused. The H and S components follow the values of the second image component. Finally, the fused image is converted from the HSV to RGB color space, and the processed image is output. The experimental results show that the proposed algorithm not only enhances the low-illumination image but also suppresses the image noise. Furthermore, it exhibits good edge preservation and obvious details.
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Key words:
- low illumination /
- image enhancement /
- Retinex /
- bilateral filtering /
- image component fusion /
- HSV color space
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图 5 弱光图像增强结果对比实验图
Figure 5. Comparison experimental diagram of low light image enhancement results
表 1 Label 1图像增强质量评价
Table 1. Label 1 image enhancement quality evaluation
Label 1 Label diagram SSR MSR Gamma Fu’s algorithm Ours PSNR 14.51 4.43 4.89 17.32 18.20 19.45 SSIM 0.58 0.35 0.40 0.82 0.87 0.92 IE 7.15 5.54 6.46 5.43 7.12 6.90 
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表 2 Label 5图像增强质量评价
Table 2. Label 5 image enhancement quality evaluation
Label 5 Label diagram SSR MSR Gamma Fu’s algorithm算法 Ours PSNR 13.41 3.70 4.48 14.00 15.41 16.56 SSIM 0.44 0.34 0.32 0.22 0.59 0.70 IE 7.2 6.29 6.62 5.37 6.34 7.04
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